Systems and methods for animating non-humanoid characters with human motion data

ABSTRACT

Systems, methods and products for animating non-humanoid characters with human motion are described. One aspect includes selecting key poses included in initial motion data at a computing system; obtaining non-humanoid character key poses which provide a one to one correspondence to selected key poses in said initial motion data; and statically mapping poses of said initial motion data to non-humanoid character poses using a model built based on said one to one correspondence from said key poses of said initial motion data to said non-humanoid character key poses. Other embodiments are described.

CLAIM FOR PRIORITY

This application is a continuation of U.S. patent application Ser. No.13/115,829, filed on May 25, 2011, which claims priority to U.S.Provisional Patent Application Ser. No. 61/348,099, filed on May 25,2010, both of which are incorporated by reference herein.

FIELD OF THE INVENTION

The subject matter described herein generally relates to non-humanoidcharacter animation.

BACKGROUND

Keyframing has been almost the only technique available to animatenon-humanoid characters. Although data-driven techniques using humanmotion capture data are popular for human animation, these techniquesare generally not applicable to non-humanoid characters for a variety ofreasons.

BRIEF SUMMARY

In summary, one aspect provides a method for animation comprising:selecting key poses included in initial motion data at a computingsystem; obtaining non-humanoid character key poses which provide a oneto one correspondence to selected key poses in said initial motion data;and statically mapping poses of said initial motion data to non-humanoidcharacter poses using a model built based on said one to onecorrespondence from said key poses of said initial motion data to saidnon-humanoid character key poses.

Another aspect provides a computer program product comprising: acomputer readable storage medium having computer readable program codeembodied therewith, the computer readable program code comprising:computer readable program code configured to access key poses selectedfrom initial motion data; computer readable program code configured toaccess non-humanoid character key poses which provide a one to onecorrespondence to selected key poses in said initial motion data; andcomputer readable program code configured to statically map poses ofsaid initial motion data to non-humanoid character poses using a modelbuilt based on said one to one correspondence from said key poses ofsaid initial motion data to said non-humanoid character key poses.

A further aspect provides a system comprising: at least one processor;and a memory device operatively connected to the at least one processor;wherein, responsive to execution of program instructions accessible tothe at least one processor, the at least one processor is configured to:access key poses selected from initial motion data; access non-humanoidcharacter key poses which provide a one to one correspondence toselected key poses in said initial motion data; and statically map posesof said initial motion data to non-humanoid character poses using amodel built based on said one to one correspondence from said key posesof said initial motion data to said non-humanoid character key poses.

The foregoing is a summary and thus may contain simplifications,generalizations, and omissions of detail; consequently, those skilled inthe art will appreciate that the summary is illustrative only and is notintended to be in any way limiting.

For a better understanding of the embodiments, together with other andfurther features and advantages thereof, reference is made to thefollowing description, taken in conjunction with the accompanyingdrawings. The scope of the invention will be pointed out in the appendedclaims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates an example animation process.

FIG. 2 illustrates an example non-humanoid character and localcoordinate system.

FIG. 3 illustrates an example mapping process.

FIG. 4(A-E) illustrates examples of animating using human motion for anon-humanoid character.

FIG. 5 illustrates an example of linear momentum for scaled humanmomentum and non-humanoid character momentum.

FIG. 6(A-B) illustrates an example of vertical contact forces before andafter optimization for required and actual contact forces.

FIG. 7 illustrates an example computer system.

DETAILED DESCRIPTION

It will be readily understood that the components of the embodiments, asgenerally described and illustrated in the figures herein, may bearranged and designed in a wide variety of different configurations inaddition to the described example embodiments. Thus, the following moredetailed description of the example embodiments, as represented in thefigures, is not intended to limit the scope of the embodiments, asclaimed, but is merely representative of example embodiments.

Reference throughout this specification to “one embodiment” or “anembodiment” (or the like) means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, appearances of the phrases “in oneembodiment” or “in an embodiment” or the like in various placesthroughout this specification are not necessarily all referring to thesame embodiment.

Furthermore, the described features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments. In thefollowing description, numerous specific details are provided to give athorough understanding of embodiments. One skilled in the relevant artwill recognize, however, that the various embodiments can be practicedwithout one or more of the specific details, or with other methods,components, materials, et cetera. In other instances, well-knownstructures, materials, or operations are not shown or described indetail to avoid obfuscation.

Embodiments provide techniques for animating non-humanoid characterswith human-like motion. A non-humanoid character may include for exampleinanimate objects, such as a lamp or a piece of furniture, an animal,such as a bird, and the like. To create anthropomorphic motion of anon-humanoid character, an embodiment may use, for example, motioncapture data of a human subject acting in the style of a targetnon-humanoid character. A few key poses are selected from the capturedmotion sequence as particularly representative, for example as selectedby an actor. Corresponding non-humanoid character poses are then createdbased on these key poses, for example by an animator on a 3D graphicssoftware (animation) system. An embodiment automatically uses the keyposes to build a statistical model for mapping of human poses tonon-humanoid character poses. An embodiment may generate a sequence ofposes by mapping every frame of the motion capture sequence using amapping function. Finally, an optimization process may be employed toadjust the fine details of the animated motion for the non-humanoidcharacter, such as contact constraints and physical realism.

An embodiment employs a statistical model called shared Gaussian processlatent variable models (shared GPLVM) to map a human pose to anon-humanoid character pose. It should be noted that although anembodiment utilizes shared GPLVM, other approaches, for exampleprincipal component analysis and linear mapping (PCA), linearinterpolation of nearest neighbors (NN), or Gaussian Process (GP) may beutilized. Embodiments may use a small set of key poses, rather thansequences, to learn a mapping function that covers a wide range ofbehaviors. Embodiments thus allow for actors and animators to createaccurate non-humanoid character animation from a sparse set of posesrather than needing to create entire motion sequences, and operate onthe view that the dynamics, or velocity information, may best come fromthe actor's captured motion.

FIG. 1 illustrates an example animation synthesis process. An embodimentfirst captures motions of a trained actor or actress performing in thestyle of the target non-humanoid character 110. For example, the actoror actress may be provided with instructions about the capability andcharacteristics of the non-humanoid character. The talents of the actoror actress may then be relied upon to portray how the non-humanoidcharacter would act in a particular situation.

A few key poses may then be selected. The key poses are selected so thatthey cover and are representative of the space of all poses that appearin the captured motions 120. A non-humanoid character pose correspondingto each of the selected key poses is then created 130.

If necessary, an animator can operate a 3D graphics software system tomanipulate the non-humanoid character pose. It may be desirable for thisprocess to be performed manually because intelligent decisions may benecessary regarding, for example, realizing the same contact states oncharacters (human vs. non-humanoid) with completely different limblengths. It may also be desirable to add poses that are not possible forthe human body, such as an extreme back bend for a character supposed tobe much more flexible than humans are. Nonetheless, the total time andlabor required for the above operations are far less than those requiredfor animating the same non-humanoid character by hand. The remainingsteps may be completed automatically without any user interaction.

An embodiment uses a statistical model to map the human pose (forexample, in each frame) of the captured motion data to a non-humanoidcharacter pose (described further herein) 140. The model may be learnedfrom the given key poses and corresponding non-humanoid character poses.An embodiment may employ a statistical modeling algorithm called sharedGaussian process latent variable model (shared GPLVM).

A mapping function converts frames of the captured motion sequence to aseries of non-humanoid character poses. Then the global transformationof the poses is obtained by matching the linear and angular momenta ofthe non-humanoid character motion to that of the human motion (describedfurther). In many cases, there are still a number of visual artifacts inthe motion, such as contact points penetrating the floor or floating inthe air. Therefore, optionally a refinement of the motion may beaccomplished by correcting the contact point positions and improving thephysical realism through an optimization process, taking into accountthe dynamics of the non-humanoid character 150. The output is animationof a non-humanoid character that is based on human motion butappropriately fit to the non-humanoid character 160.

Static Mapping

An embodiment learns the static mapping of human poses to non-humanoidcharacter poses. An embodiment may employ a statistical method calledshared Gaussian latent variable model (shared GPLVM) to learn the staticmapping function from a human pose to a non-humanoid character pose.Shared GPLVM is suitable because human poses and correspondingnon-humanoid character poses likely have some underlying nonlinearrelationship. Moreover, shared GPLVM gives a probability distributionover the non-humanoid character poses, which can potentially be used foradjusting the non-humanoid character pose to satisfy other constraints.

Shared GPLVM is an extension of GPLVM, which models the nonlinearmapping from a low dimensional space (latent space) to an observationspace. Shared GPLVM extends GPLVM by allowing multiple observationspaces sharing a common latent space. Similar models have been used forgenerating images from different viewpoints and mapping human motions tohumanoid robot motions.

A main objective of using shared GPLVM in prior work was to limit theoutput space with ambiguity due to, for example, monocular video. Anembodiment may adopt shared GPLVM because the sparse set ofcorresponding key poses probably does not represent the valid pose spaceon its own. It can be expected that there is a common causal structurebetween human and non-humanoid character motions. In addition, it isknown that a wide variety of human motions are confined to a relativelylow-dimensional space. A model with shared latent space is an effectiveway to discover and model the space that represents the underlyingstructure.

The mapping problem involves two observation spaces: theD_(Y)-dimensional human pose space and the D_(Z)-dimensionalnon-humanoid character pose space. These spaces are associated with aD_(X)-dimensional latent space. In contrast to the existing techniquesthat use time-series data for learning a model, a challenge here is thatthe given samples are very sparse compared to the complexity of thehuman and non-humanoid character models.

Motion Representation

There are several options to represent poses of human and non-humanoidcharacter models. In an example embodiment, the Cartesian positions ofmultiple feature points on the human and non-humanoid character bodieswere utilized. For the human model, motion capture markers may be usedbecause marker sets are usually designed so that they can representhuman poses well. Similarly, a set of virtual markers may be defined forthe non-humanoid character model, for example by placing three markerson each link of the skeleton, and their positions may be used torepresent non-humanoid character poses.

The Cartesian positions are converted to a local coordinate frame tomake them invariant to global transformations. It is assumed herein thatthe height and roll/pitch angles are important features of a pose, andtherefore only the horizontal position and yaw angle are canceled out.For this purpose, a local coordinate frame is determined to representthe feature point positions.

For a non-humanoid character (a lamp in FIG. 2), the local coordinate(solid) is determined based on the root position and orientation asfollows (referring generally to FIG. 2). It is assumed that two localvectors are defined for the root joint: the front and up vectors(dashed) that point in the front and up directions of the model. Theposition of the local coordinate is simply the projection of the rootlocation to a horizontal plane with a constant height. The z axis of thelocal coordinate points in the vertical direction. The x axis faces theheading direction of the root joint, which is found by first obtainingthe single axis rotation to make the up vector vertical, and thenapplying the same rotation to the front vector. The y axis is chosen toform a right-hand system.

For each key pose i, the observation vectors y_(i) and z_(i) are formedby concatenating the local-coordinate Cartesian position vectors of thefeature points of the human and non-humanoid character models,respectively. Then the vectors for all key poses are collected to formobservation matrices Y and Z. The latent coordinates associated with theobservations are denoted by X.

Learning and Mapping

Example learning and mapping processes are outlined in FIG. 3, where theinputs are drawn with a black background. In the learning process, theparameters of the GPLVMs and the latent coordinates for each key poseare obtained by maximizing the likelihood of generating the given pairof key poses. In the mapping process, the latent coordinates for eachmotion capture frame that maximize the likelihood of generating thegiven human pose are obtained. The latent coordinates are then used tocalculate the non-humanoid character pose using GPLVM. An issue inshared GPLVM is how to determine the dimension of the latent space.Several criteria that may be used for this purpose are described furtherherein.

Learning

A GPLVM parameterizes the nonlinear mapping function from the latentspace to observation space by a kernel matrix. The (i,j) element of thekernel matrix K represents the similarity between two data points in thelatent space x_(i) and x_(j), and is calculated by:

$\begin{matrix}{K_{ij} = {{k( {x_{i},x_{j}} )} = {{\theta_{1}\exp\{ {\frac{\theta_{2}}{2}{{x_{i} - x_{j}}}^{2}} \}} + \theta_{3} + {\beta^{- 1}\delta_{ij}}}}} & (1)\end{matrix}$where Φ=k(x_(i), x_(j))={θ₁, θ₂, θ₃, β} are the model parameters and δrepresents the delta function. The parameters of the mapping functionsfrom latent space to human pose are denoted by Φ_(Y) and from latentspace to character pose by Φ_(Z).

Assuming a zero-mean Gaussian process prior on the functions thatgenerates the observations from a point in the latent space, thelikelihoods of generating the given observations are formulated as:

${P( { Y \middle| X ,\Phi_{Y}} )} = {\frac{1}{\sqrt{( {2\pi} )^{{ND}_{Z}}{K_{Z}}^{D_{Z}}}}\exp\{ {{- \frac{1}{2}}{\sum\limits_{k = 1}^{D_{Y}}{y_{k}^{T}K_{Y}^{- 1}y_{k}}}} \}}$${P( { Z \middle| X ,\Phi_{Z}} )} = {\frac{1}{\sqrt{( {2\pi} )^{{ND}_{Z}}{K_{z}}^{D_{Z}}}}\exp\{ {{- \frac{1}{2}}{\sum\limits_{k = 1}^{D_{Z}}{z_{k}^{T}K_{Z}^{- 1}z_{k}}}} \}}$where K_(Y) and K_(Z) are the kernel matrices calculated using equation(1) with Φ_(Y) and Φ_(Z) respectively, and y_(k) and z_(k) denote thek-th dimension of the observation matrices Y and Z, respectively. Usingthese likelihoods and priors for Φ_(Y), Φ_(Z) and X, the jointlikelihood can be calculated as:P _(GP)(Y,Z|X,Φ _(Y),Φ_(Z))=P(Y|X,Φ _(Y))P(Z|X,Φ_(Z))P(Φ_(Y))P(Φ_(Z))P(X).  (2)

Learning shared GPLVM is essentially an optimization process to obtainthe model parameters Φ_(Y), Φ_(Z) and latent coordinates X that maximizethe joint likelihood. The latent coordinates are initialized usingKernel Canonical Correlation Analysis (CCA).

After the model parameters Φ_(Z) are learned, the probabilitydistribution of the character pose for given latent coordinates x may beobtained by:z (x)=μ_(Z) +Z ^(T) K _(Z) ⁻¹ k(x)  (3)σ_(Z) ²(x)=k(x,x)−k(x)^(T) K _(Z) ⁻¹ k(x)  (4)where z and σ_(Z) ² the mean and variance of the distributionrespectively, μ_(Z) is the mean of the observations, and k(x) is avector whose i-th element is k_(i)(x)=k(x,x_(i)).

Mapping

An example mapping process starts by obtaining the latent coordinatesthat correspond to a new human pose using a method combining nearestneighbor search and optimization. For a new human pose y_(new), anembodiment searches for the key pose y_(i) with the smallest Euclideandistance to y_(new). The latent coordinates associated with y_(i) areused as the initial value for the gradient-based optimization process toobtain the latent coordinates {circumflex over (x)} that maximize thelikelihood of generating y_(new), that is:

$\begin{matrix}{\hat{x} = {\arg\;{\max\limits_{x}\;{P( { y_{new} \middle| x ,Y,X,\Phi_{Y}} )}}}} & (5)\end{matrix}$The latent coordinates {circumflex over (x)} are used to obtain thedistribution of the non-humanoid character pose using equations (3) and(4).

Dynamics Optimization

The sequence of poses obtained so far does not include the globalhorizontal movement. It also does not preserve the contact constraintsin the original human motion because they are not considered in thestatic mapping function.

Accordingly, dynamics optimization may be performed to address theseissues. For example, the global transformation of the non-humanoidcharacter is first performed based on the linear and angular momenta ofthe original human motion. Then the contact point positions arecorrected based on the contact information. Finally, the physicalplausibility is improved by solving an optimization problem based on theequations of motion of the non-humanoid character, a penalty-basedcontact force model, and the probability distribution given by thestatic mapping function.

Global Transformation

The global transformation (position and orientation) of the non-humanoidcharacter is determined so that the linear and angular momenta of thenon-humanoid character match those obtained by scaling the momenta inthe human motion. A global coordinate system whose z axis points in thevertical direction is assumed, and x and y axes are chosen to form aright-hand system.

This step may determine the linear and angular velocities, ν and ω, ofthe local coordinate frame (described herein in connection with FIG. 2).The linear and angular momenta of the character at frame i in the resultof the static mapping are denoted by P_(c)(i) and L_(c)(i),respectively. If the local coordinate moves at ν(i) and ω(i), themomenta would change to:{circumflex over (P)} _(c)(i)=P _(c)(i)+m _(c)ν(i)+ω(i)×p(i)  (6){circumflex over (L)} _(c)(i)=L _(c)(i)+I _(c)(i)ω(i)  (7)where m_(c) is the total mass of the character, p(i) is the whole-bodycenter of mass position represented in the local coordinate, andI_(c)(i) is the moment(s) of inertia of the character around the localcoordinate's origin. Evaluating these equations requires the inertialparameters of individual links of the non-humanoid character model,which can be specified manually or automatically from the density andvolume of the links.

Both ν(i) and ω(i) are determined so that P_(c)(i) and L_(c)(i) matchthe linear and angular momenta in the original human motion capturedata, P_(h)(i) and L_(h)(i), after applying appropriate scaling toaddress the difference in kinematics and dynamics parameters. A methodthat may be used to obtain the scaling parameters will be describedherein. Given the scaled linear and angular momenta P_(h)(i) andL_(h)(i), ν(i) and ω(i) can be obtained by solving a linear equation:

$\begin{matrix}{{\begin{pmatrix}{m_{c}E} & {- \lbrack {{p(i)} \times} \rbrack} \\0 & {I_{c}(i)}\end{pmatrix}\begin{pmatrix}{v(i)} \\{\omega(i)}\end{pmatrix}} = \begin{pmatrix}{{{\hat{P}}_{h}(i)} - {{\hat{P}}_{c}(i)}} \\{{{\hat{L}}_{h}(i)} - {{\hat{L}}_{c}(i)}}\end{pmatrix}} & (8)\end{matrix}$where E is the 3×3 identity matrix. Both ν(i) and ω(i) are integrated toobtain the position and orientation of the local coordinate in the nextframe. In an example implementation, the horizontal transformation isonly considered. That is, linear velocity in the x and y directions andthe angular velocity around the z axis are only considered, because theother translation and rotation degrees of freedom are preserved in thekey poses used for learning, and therefore appear in the static mappingresults. The appropriate rows and columns are extracted from equation(8) to remove the irrelevant variables.

The scaling factors may be obtained from size, mass, and inertia ratiosbetween the human and non-humanoid character models. Mass ratio iss_(m)=m_(c)/m_(h) where m_(h) is the total mass of the human model. Theinertia ratio consists of three values corresponding to the threerotational axes in the global coordinate. To calculate the inertiaratio, the moments of inertia of the human model around its localcoordinate, I_(h)(i), are obtained. The ratio of the diagonal elements

(s_(ix) s_(iy) s_(iz))^(T)=(I_(cxx)/I_(hxx) I_(cyy)/I_(hyy)I_(czz)/I_(hzz))^(T) are used as the inertia ratio. The size ratio alsoconsists of three values representing the ratios in depth (along x axisof the local coordinate), width (y axis), and height (z axis). Becausetopological correspondence between the human and non-humanoid charactermodels cannot be assumed, an embodiment calculates the average featurepoint velocity for each model when every degree of freedom is rotated ata unit velocity one by one. The size scale is then obtained from thevelocities ν_(h) for the human model and ν_(c) for the non-humanoidcharacter model as (s_(dx) s_(dy) s_(dz))^(T)=(ν_(cx)/ν_(hx)ν_(cy)/ν_(hy) ν_(cz)/ν_(hz))^(T). Using these ratios, the scaled momentaare obtained as {circumflex over (P)}_(h)*=s_(m)s_(d)*P_(h)*,L_(h)*=s_(i)*L_(h)* where *={x, y, z}.

Contact Point Adjustment

An embodiment may adjust the poses so that the points in contact stay atthe same position on the floor, using the contact states in the originalhuman motion. It is assumed that a corresponding human contact point isgiven for each of the potential contact points on the non-humanoidcharacter. Potential contact points are typically chosen from the toesand heels, although other points may be added if other parts of the bodyare in contact. Manual determination of the contact and flight phases ofeach point may be used, although some automatic algorithms or additionalcontact sensors may be employed.

Once the contact and flight phases are determined for each contactpoint, the corrected position is calculated. For each contact phase, anembodiment calculates the average position during the contact phase, anduses its projection to the floor as the corrected position. To preventdiscontinuities due to the correction, the contact point positions maybe modified while the character is in flight phase by smoothlyinterpolating the position corrections at the end of the precedingcontact phase Δc₀ and at the beginning of the following one Δc₁ as:ĉ(t)=c(t)+(1−w(t))Δc ₀ +w(t)Δc ₁  (9)where c and ĉ are the original and modified positions, respectively, andw(t) is a weighting function that smoothly transitions from 0 to 1 asthe time t moves from the start time t₀ of the flight phase to the endtime t₁. As an example, an embodiment may use w(t)=h²(3−2h) whereh=(t−t₀)/(t₁−t₀).

Optimizing the Physical Realism

An embodiment may improve the physical realism by adjusting the verticalmotion of the root so that the motion is consistent with the gravity anda penalty-based contact model. The position displacement from theoriginal motion is represented along a single axis by a set of Nweighted radial basis functions (RBFs). For example, an embodiment mayuse a Gaussian for RBFs, in which case the displacement Δz is calculatedas:

$\begin{matrix}{{{\Delta\;{z(t)}} = {\sum\limits_{i = 1}^{N}{w_{i}{\phi_{i}(t)}}}},{{\phi_{i}(t)} = {\exp\{ {- \frac{( {t - T_{i}} )^{2}}{\sigma^{2}}} \}}}} & (10)\end{matrix}$where T_(i) is the center of the i-th Gaussian function and σ is thestandard deviation of the Gaussian functions. As an example, anembodiment places the RBFs with a constant interval along the time axisand sets σ to be twice that of the interval. The vector composed by theRBF weights is denoted as w=(w_(i) w₂ . . . w_(N))^(T).

The optimization may obtain the weights w that optimize three criteria:(1) preserve the original motion as much as possible, (2) maximize thephysical realism, and (3) maximize the likelihood with respect to thedistribution output by the mapping function. As such, the cost functionto minimize is:Z=½w ^(T) w+k ₁ Z _(p) +k ₂ Z _(m)  (11)where the first term of the right hand side tries to keep the weightssmall, and the second and third terms address the latter two criteria ofthe optimization. Parameters k₁ and k₂ are user-defined positiveconstants.

Z_(p) may be used to maximize the physical realism and is given by:Z _(p)=½{(F−{circumflex over (F)})^(T)(F−{circumflex over(F)})+(N−{circumflex over (N)})^(T)(N−{circumflex over (N)})}  (12)where F and N are the total external force and moment required toperform the motion, and {circumflex over (F)} and {circumflex over (N)}are the external force and moment from the contact forces.

An embodiment may calculate F and N by performing the standard inversedynamics calculation and extracting the 6-axis force and moment at theroot joint.

An embodiment may calculate {circumflex over (F)} and {circumflex over(N)} from the positions and velocities of the contact points on thenon-humanoid character (described herein), based on a penalty-basedcontact model. The normal contact force at a point whose height from thefloor is z (z<0 if penetrating) is given by:

$\begin{matrix}{{{f_{n}( {z,\overset{.}{z}} )} = {{\frac{1}{2}{k_{P}( {\sqrt{z^{2} + \frac{4f_{0}^{2}}{k_{P}^{2}}} - z} )}} - {k_{D}{g(z)}\overset{.}{z}}}}{{g(z)} = \{ \begin{matrix}{1 - {\frac{1}{2}{\exp({kz})}}} & ( {z < 0} ) \\{\frac{1}{2}{\exp( {- {kz}} )}} & ( {0 \leq z} )\end{matrix} }} & (13)\end{matrix}$where the first and second terms of equation (13) correspond to thespring and damper forces, respectively. When ż=0, the asymptote ofequation (13) is f_(n)=−k_(pz) for z→−∞, and f_(n)=0 for z→+∞, which isthe behavior of the standard linear spring contact model with springcoefficient k_(p). The formulation adopted here smoothly connects thetwo functions to produce a continuous force across the state space. Theconstant parameter f₀ denotes the residual contact force at z=0 andindicates the amount of error from the linear spring contact model. Thesecond term of equation (13) acts as a linear damper, except that theactivation function g(z) continuously reduces the force when thepenetration depth is small or the point is above the floor. The springand damping coefficients are generally chosen so that the groundpenetration does not cause visual artifacts.

The friction force f_(t) is formulated as:

$\begin{matrix}{{{f_{t}( {r,\overset{.}{r}} )} = {\mu\; f_{n}{\hat{F}}_{t}}}{{\hat{F}}_{t} = {{h( f_{t\; 0} )}F_{t\; 0}}}{{f_{t\; 0} = {F_{t\; 0}}},{F_{t\; 0} = {{- {k_{tP}( {r - \hat{r}} )}} - {k_{tD}\overset{.}{r}}}}}{{h( f_{t\; 0} )} = \{ \begin{matrix}\frac{1 - {\exp( {{- k_{t}}f_{t\; 0}} )}}{f_{t\; 0}} & ( {ɛ < f_{t\; 0}} ) \\k_{t} & ( {f_{t\; 0} < ɛ} )\end{matrix} }} & (14)\end{matrix}$where r is a two-dimensional vector representing the contact pointposition on the floor, {circumflex over (r)} is nominal position of thecontact point, μ is the friction coefficient, k_(t), k_(tP) and k_(tD)are user-specified positive constants, and ε is a small positiveconstant. Friction force is usually formulated as μf_(n)F_(t0)/f_(t0),which is a vector with magnitude μf_(n) and direction F_(t0). To solvethe singularity problem at f_(t0)=0, an embodiment may introduced thefunction h(f_(t0)) that approaches 1/f_(t0) as f_(t0)→∞ and some finitevalue k_(t) as f_(t0)→0. The optimization is generally insensitive tothe parameters used in equation (14).

The last term of equation (11), Z_(m), represents the negativelog-likelihood of the current pose, that is:

$\begin{matrix}{Z_{m} = {- {\sum\limits_{i}{\log\;{P( z_{i} )}}}}} & (15)\end{matrix}$where i denotes the frame number and z_(i) is the position vector in theobservation space formed from the feature points positions of thecharacter at frame i. Function P(z) gives the likelihood of generating agiven vector z from the distribution given by equations (3) and (4).

It should be briefly noted that for dynamics optimization, there aremany other options for each component. For example, although adjustmentof the vertical motion of the root has been described, embodiments mayalso adjust other directions and/or joints; the displacement may berepresented other ways, such as B-spline; a more detailed dynamics modelof the non-humanoid character may be employed instead of just linear andangular momentum, et cetera.

EXAMPLES

Embodiments may be used to animate non-humanoid characters such as alamp, a penguin, a squirrel and the like. For example, a lampnon-humanoid character is an example of character inspired by anartificial object but yet able to perform human-like expressions usingthe arm and lampshade as body and face. The completely differenttopology and locomotion style from human topology and style make itdifficult to animate this non-humanoid character. A penguin characterhas human-like topology but the limbs are extremely short, with limitedmobility. Although it still does biped walking, its locomotion style isalso very different from humans because of its extremely short legs. Asquirrel character has human-like topology but may also walk on fourlegs. The tail may also be occasionally animated during the key posecreation process.

An example software system includes the following components: a C++ codelibrary for reading motion capture data and key poses, converting themto feature point data, computing the inverse kinematics, and evaluatingZ_(p) of the cost function; a publicly available MATLAB implementationof the learning and mapping functions of shared GPLVM; and MATLAB codefor evaluating Z_(m) of the cost function and performing theoptimization using the MATLAB function lsqnonlin.

The parameters used in the examples are as follows: for equation (11):k₁=1×10⁻⁵, k₂=1, for equation (13): k_(P)=1×10⁴, k_(D)=1, f₀=1, k=20,and for equation (14): μ=1, k_(tP)=0, k_(tD)=100, k_(t)=20, ε=1×10⁻⁶.

The motions of a professional actor expressing six emotions (anger,disgust, fear, happiness, sadness and surprise) were recorded for eachof the three non-humanoid characters mentioned here. Before the motioncapture session, the actor was shown a picture of each non-humanoidcharacter and the kinematic properties were explained (for example, noor extremely short legs, may walk on four legs, et cetera). The capturesession lasted about two hours.

For static mapping, an example embodiment was trained using a sharedGPLVM for each non-humanoid character using the key poses created by theactor and animator. An issue in using GPLVM is how to determine thedimension of the latent space, D_(X). As an example, two criteria may beused to determine D_(X).

The first criteria is the error between the given and mapped characterkey poses. Error generally improved by increasing D_(X) up to 15, butdid not improve much more at D_(X)>15. It was also found that 70iterations are enough for optimizing the model parameters.

Another desired property is that the non-humanoid character motionbecomes continuous when the human motion is continuous. Regarding thedimensions of the trajectories in the latent space when a human motioncapture sequence is input to the models, the 2-dimensional space, oftenused in the literature, is not enough to describe the wide range ofpostures found in example data sets. Although a 15-dimensional space isenough to reconstruct the training data, the trajectory in the latentspace is still jerky and results in unnatural jumps in the non-humanoidcharacter motion. Therefore, a 30-dimensional space may be used.

FIG. 4A-B illustrates snapshots of an original human pose and theresults of static mapping for a non-humanoid character (a lamp characterin this example). Note that the non-humanoid character stays above afixed point on the floor because the horizontal movement has beenremoved from the key poses before learning the mapping function. Theactor and an animator may work together with a 3D graphics softwaresystem to select key poses from the motion capture data and createcorresponding poses for the non-humanoid characters.

FIG. 4C-E illustrates the results of each step in the dynamicsoptimization process. First, a horizontal movement is attached to themapped poses as shown in FIG. 4C. The contact point positions arecorrected using the contact state information in the human motion asshown in FIG. 4D. Finally, an optimization process takes place to addphysical realism to the motion and the result shown in FIG. 4E.

Some of the numerical results during this example process areillustrated in FIGS. 5-6(A-B). FIG. 5 shows the scaled human momentum(dashed line) and non-humanoid character momentum (solid line) in theforward direction. The two graphs in FIG. 6(A-B) show the requiredcontact force (calculated by inverse dynamics) and the contact forcefrom the contact model. FIG. 6(A-B) denotes the forces before (FIG. 6A)and after (FIG. 6B) the optimization. The optimization process tries tomatch the two forces by modifying the trajectory, which changes both therequired (dashed line) and actual contact (solid line) forces.

It should be understood that embodiments may be implemented as a system,method, apparatus or computer program product. Accordingly, variousembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects. Furthermore, embodiments may take the form of a computerprogram product embodied in one or more computer readable medium(s)having computer readable program code embodied therewith.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a non-signal computerreadable medium, referred to herein as a computer readable storagemedium. A computer readable storage medium may be, for example, but notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, or device, or any suitablecombination of the foregoing. A computer readable storage medium may beany tangible medium that can contain or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

Computer program code for carrying out operations of various embodimentsmay be written in any combination of one or more programming languages(including an object oriented programming language such as Java™,Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages). The program code may execute entirely on the user's computer(device), partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. The remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer.

It will be understood that certain embodiments can be implemented by adevice such as a computer executing a program of instructions. Thesecomputer program instructions may be provided to a processor of aspecial purpose computer, or other programmable data processingapparatus to produce a machine, such that the instructions, whichexecute via the processor of the computer or other programmable dataprocessing apparatus, implement the functions/acts specified.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus or the like to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified.

In this regard, referring now to FIG. 7, an example device that may beused in connection with one or more embodiments includes a computingdevice in the form of a computer 710. Components of computer 710 mayinclude, but are not limited to, a processing unit 720, a system memory730, and a system bus 722 that couples various system componentsincluding the system memory 730 to the processing unit 720. Computer 710may include or have access to a variety of computer readable media. Thesystem memory 730 may include computer readable storage media, forexample in the form of volatile and/or nonvolatile memory such as readonly memory (ROM) and/or random access memory (RAM). By way of example,and not limitation, system memory 730 may also include an operatingsystem, application programs, other program modules, and program data.

A user can interface with (for example, enter commands and information)the computer 710 through input devices 740. A monitor or other type ofdevice can also be connected to the system bus 722 via an interface,such as an output interface 750. In addition to a monitor, computers mayalso include other peripheral output devices. The computer 710 mayoperate in a networked or distributed environment using logicalconnections to one or more other remote device(s) 770 such as othercomputers. The logical connections may include network interface(s) 760to a network, such as a local area network (LAN), a wide area network(WAN), and/or a global computer network, but may also include othernetworks/buses.

This disclosure has been presented for purposes of illustration anddescription but is not intended to be exhaustive or limiting. Manymodifications and variations will be apparent to those of ordinary skillin the art. The embodiments were chosen and described in order toexplain principles and practical application, and to enable others ofordinary skill in the art to understand the disclosure for variousembodiments with various modifications as are suited to the particularuse contemplated.

Although illustrative embodiments have been described herein, it is tobe understood that the embodiments are not limited to those preciseembodiments, and that various other changes and modifications may beaffected therein by one skilled in the art without departing from thescope or spirit of the disclosure.

What is claimed is:
 1. A method for animation comprising: selecting keyposes included in initial motion data at a computing system; obtainingnon-humanoid character key poses which provide a one to onecorrespondence to selected key poses in said initial motion data; andstatically mapping poses of said initial motion data in a firstobservational space to non-humanoid character poses in a secondobservation space using a model with shared latent space in a lowdimensional space built based on said one to one correspondence fromsaid key poses of said initial motion data to said non-humanoidcharacter key poses; wherein said model with shared latent spacediscovers and models space that represents a common causal structurebetween human and non-humanoid character motion.
 2. The method accordingto claim 1, further comprising optimizing statically mapped data for anon-humanoid character to create a physically correct animated motionfor said non-humanoid character.
 3. The method according to claim 1,wherein said initial motion data is derived from one or more of: amotion capture system, and a key framing process.
 4. The methodaccording to claim 3, wherein the initial motion data is human motioncapture data.
 5. The method according to claim 1, wherein said selectingkey poses included in said initial motion data is performed by a useroperating an animation system.
 6. The method according to claim 1,wherein said non-humanoid character key poses are derived from anexternal device.
 7. The method according to claim 1, wherein saidnon-humanoid character key poses comprise key poses created by ananimator collaborating with one or more actors.
 8. The method accordingto claim 1, wherein said statically mapping further comprises learning astatic mapping function from poses of said initial motion data to saidnon-humanoid character poses.
 9. The method according to claim 8,wherein said model is a shared Gaussian process latent variable model.10. The method according to claim 2, wherein said optimizing staticallymapped data for a non-humanoid character further comprises one or moreof: determining a global transformation of said non-humanoid characterposes; correcting one or more contact positions; and optimizing physicalrealism of non-humanoid character motion by adjusting vertical motion tobe consistent with gravity and a penalty-based contact model.
 11. Acomputer program product comprising: a non-transitory computer readablestorage medium having computer readable program code embodied therewith,the computer readable program code comprising: computer readable programcode configured to access key poses selected from initial motion data;computer readable program code configured to access non-humanoidcharacter key poses which provide a one to one correspondence toselected key poses in said initial motion data; and computer readableprogram code configured to statically map poses of said initial motiondata in a first observational space to an non-humanoid character posesin a second observation space using a model with shared latent space ina low dimensional space built based on said one to one correspondencefrom said key poses of said initial motion data to said non-humanoidcharacter key poses; wherein said model with shared latent spacediscovers and models space that represents a common causal structurebetween human and non-humanoid character motion.
 12. The computerprogram product according to claim 11, further comprising computerreadable program code configured to optimize statically mapped data fora non-humanoid character to create a physically correct animated motionfor said non-humanoid character.
 13. The computer program productaccording to claim 11, wherein said initial motion data is derived fromone or more of: a motion capture system, and a key framing process. 14.The computer program product according to claim 12, wherein the initialmotion data is human motion capture data.
 15. The computer programproduct according to claim 12, wherein said mapping further compriseslearning a static mapping function from poses of said initial motiondata to said non-humanoid character poses.
 16. The computer programproduct according to claim 15, wherein said model is a shared Gaussianprocess latent variable model.
 17. The computer program productaccording to claim 12, wherein said optimizing statically mapped datafor a non-humanoid character further comprises one or more of:determining a global transformation of said non-humanoid characterposes; correcting one or more contact positions; and optimizing physicalrealism of non-humanoid character motion by adjusting motion of saidnon-humanoid character to be consistent with gravity and a penalty-basedcontact model.
 18. The computer program product according to claim 17,further comprising computer readable program code configured to matchlinear and angular momenta of said non-humanoid character poses to posesobtained by scaling momenta in poses of said initial motion data. 19.The computer program product according to claim 17, wherein saidoptimizing physical realism of said non-humanoid character motionfurther comprises adjusting vertical motion of said non-humanoidcharacter.
 20. A system comprising: at least one processor; and a memorydevice operatively connected to the at least one processor; wherein,responsive to execution of program instructions accessible to the atleast one processor, the at least one processor is configured to: accesskey poses selected from initial motion data; access non-humanoidcharacter key poses which provide a one to one correspondence toselected key poses in said initial motion data; and statically map posesof said initial motion data in a first observational space tonon-humanoid character poses in a second observation space using a modelwith shared latent space in a low dimensional space built based on saidone to one correspondence from said key poses of said initial motiondata to said non-humanoid character key poses; wherein said model withshared latent space discovers and models space that represents a commoncausal structure between human and non-humanoid character motion.